Merge branch 'dev' into cop_eval_all_levels

src/gluonts/mx/prelude.py

@@ -11,7 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-# flake8: noqa: F401
+# flake8: noqa: F401, F403
 
 from .component import *
 from .serde import *

src/gluonts/nursery/daf/requirements.txt

@@ -3,5 +3,5 @@ tensorboard==2.3.0
 numpy==1.22.0
 pandas==1.1.5
 scikit-learn==0.23.2
-scipy==1.5.2
+scipy==1.10.0
 matplotlib==3.3.2

src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/_estimator.py

@@ -13,7 +13,7 @@
 
 from copy import deepcopy
 from functools import partial
-from typing import List, Tuple
+from typing import List, Tuple, Optional
 
 from mxnet.gluon import HybridBlock
 import numpy as np
@@ -41,8 +41,9 @@
  SelectFields,
  SimpleTransformation,
  Transformation,
+ MissingValueImputation,
+ RollingMeanValueImputation,
 )
-
 from gluonts.nursery.temporal_hierarchical_forecasting.model.cop_deepar.gluonts_fixes import (
  batchify_with_dict,
  DeepAREstimatorForCOP,
@@ -171,6 +172,9 @@ def __init__(
  return_forecasts_at_all_levels: bool = False,
  naive_reconciliation: bool = False,
  dtype: Type = np.float32,
+ impute_missing_values: bool = False,
+ imputation_method: Optional[MissingValueImputation] = None,
+ num_imputation_samples: int = 1,
  ) -> None:
  super().__init__(trainer=trainer, dtype=dtype)
 
@@ -202,11 +206,20 @@ def __init__(
 
  assert self.base_estimator_type == DeepAREstimatorForCOP
 
- if "distr_output" not in base_estimator_hps:
- base_estimator_hps["distr_output"] = GaussianOutput()
+ base_estimator_hps.setdefault("distr_output", GaussianOutput())
 
  print(f"Distribution output: {base_estimator_hps['distr_output']}")
 
+ base_estimator_hps.setdefault(
+ "impute_missing_values", impute_missing_values
+ )
+
+ base_estimator_hps.setdefault("imputation_method", imputation_method)
+
+ base_estimator_hps.setdefault(
+ "num_imputation_samples", num_imputation_samples
+ )
+
  self.estimators = []
  for agg_multiple, freq_str in zip(
  self.temporal_hierarchy.agg_multiples,
@@ -223,6 +236,14 @@ def __init__(
  num_nodes = self.temporal_hierarchy.num_leaves // agg_multiple
  lags_seq = [lag for lag in lags_seq if lag >= num_nodes]
 
+ # adapt window_length if RollingMeanValueImputation is used
+ if isinstance(imputation_method, RollingMeanValueImputation):
+ base_estimator_hps_agg[
+ "imputation_method"
+ ] = RollingMeanValueImputation(
+ window_size=imputation_method.window_size // agg_multiple
+ )
+
  # Hack to enforce correct serialization of lags_seq and history length
  # (only works when set in constructor).
  if agg_multiple != 1:

src/gluonts/torch/model/estimator.py

@@ -213,10 +213,15 @@ def train_model(
  ckpt_path=ckpt_path,
  )
 
- logger.info(f"Loading best model from {checkpoint.best_model_path}")
- best_model = training_network.load_from_checkpoint(
- checkpoint.best_model_path
- )
+ if checkpoint.best_model_path != "":
+ logger.info(
+ f"Loading best model from {checkpoint.best_model_path}"
+ )
+ best_model = training_network.load_from_checkpoint(
+ checkpoint.best_model_path
+ )
+ else:
+ best_model = training_network
 
  return TrainOutput(
  transformation=transformation,

src/gluonts/torch/prelude.py

@@ -11,7 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-# flake8: noqa: F401
+# flake8: noqa: F401, F403
 
 from .component import *
 from .model.forecast_generator import *